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A gamma-distribution convolution model of (99m)Tc-MIBI thyroid time-activity curves

BACKGROUND: The convolution approach to thyroid time-activity curve (TAC) data fitting with a gamma distribution convolution (GDC) TAC model following bolus intravenous injection is presented and applied to (99m)Tc-MIBI data. The GDC model is a convolution of two gamma distribution functions that si...

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Autores principales: Wesolowski, Carl A., Wanasundara, Surajith N., Wesolowski, Michal J., Erbas, Belkis, Babyn, Paul S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5161052/
https://www.ncbi.nlm.nih.gov/pubmed/27987183
http://dx.doi.org/10.1186/s40658-016-0166-z
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author Wesolowski, Carl A.
Wanasundara, Surajith N.
Wesolowski, Michal J.
Erbas, Belkis
Babyn, Paul S.
author_facet Wesolowski, Carl A.
Wanasundara, Surajith N.
Wesolowski, Michal J.
Erbas, Belkis
Babyn, Paul S.
author_sort Wesolowski, Carl A.
collection PubMed
description BACKGROUND: The convolution approach to thyroid time-activity curve (TAC) data fitting with a gamma distribution convolution (GDC) TAC model following bolus intravenous injection is presented and applied to (99m)Tc-MIBI data. The GDC model is a convolution of two gamma distribution functions that simultaneously models the distribution and washout kinetics of the radiotracer. The GDC model was fitted to thyroid region of interest (ROI) TAC data from 1 min per frame (99m)Tc-MIBI image series for 90 min; GDC models were generated for three patients having left and right thyroid lobe and total thyroid ROIs, and were contrasted with washout-only models, i.e., less complete models. GDC model accuracy was tested using 10 Monte Carlo simulations for each clinical ROI. RESULTS: The nine clinical GDC models, obtained from least counting error of counting, exhibited corrected (for 6 parameters) fit errors ranging from 0.998% to 1.82%. The range of all thyroid mean residence times (MRTs) was 212 to 699 min, which from noise injected simulations of each case had an average coefficient of variation of 0.7% and a not statistically significant accuracy error of 0.5% (p = 0.5, 2-sample paired t test). The slowest MRT value (699 min) was from a single thyroid lobe with a tissue diagnosed parathyroid adenoma also seen on scanning as retained marker. The two total thyroid ROIs without substantial pathology had MRT values of 278 and 350 min overlapping a published (99m)Tc-MIBI thyroid MRT value. One combined value and four unrelated washout-only models were tested and exhibited R-squared values for MRT with the GDC, i.e., a more complete concentration model, ranging from 0.0183 to 0.9395. CONCLUSIONS: The GDC models had a small enough TAC noise-image misregistration (0.8%) that they have a plausible use as simulations of thyroid activity for querying performance of other models such as washout models, for altered ROI size, noise, administered dose, and image framing rates. Indeed, of the four washout-only models tested, no single model approached the apparent accuracy of the GDC model using only 90 min of data. Ninety minutes is a long gamma-camera acquisition time for a patient, but a short a time for most kinetic models. Consequently, the results should be regarded as preliminary.
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spelling pubmed-51610522017-01-03 A gamma-distribution convolution model of (99m)Tc-MIBI thyroid time-activity curves Wesolowski, Carl A. Wanasundara, Surajith N. Wesolowski, Michal J. Erbas, Belkis Babyn, Paul S. EJNMMI Phys Original Research BACKGROUND: The convolution approach to thyroid time-activity curve (TAC) data fitting with a gamma distribution convolution (GDC) TAC model following bolus intravenous injection is presented and applied to (99m)Tc-MIBI data. The GDC model is a convolution of two gamma distribution functions that simultaneously models the distribution and washout kinetics of the radiotracer. The GDC model was fitted to thyroid region of interest (ROI) TAC data from 1 min per frame (99m)Tc-MIBI image series for 90 min; GDC models were generated for three patients having left and right thyroid lobe and total thyroid ROIs, and were contrasted with washout-only models, i.e., less complete models. GDC model accuracy was tested using 10 Monte Carlo simulations for each clinical ROI. RESULTS: The nine clinical GDC models, obtained from least counting error of counting, exhibited corrected (for 6 parameters) fit errors ranging from 0.998% to 1.82%. The range of all thyroid mean residence times (MRTs) was 212 to 699 min, which from noise injected simulations of each case had an average coefficient of variation of 0.7% and a not statistically significant accuracy error of 0.5% (p = 0.5, 2-sample paired t test). The slowest MRT value (699 min) was from a single thyroid lobe with a tissue diagnosed parathyroid adenoma also seen on scanning as retained marker. The two total thyroid ROIs without substantial pathology had MRT values of 278 and 350 min overlapping a published (99m)Tc-MIBI thyroid MRT value. One combined value and four unrelated washout-only models were tested and exhibited R-squared values for MRT with the GDC, i.e., a more complete concentration model, ranging from 0.0183 to 0.9395. CONCLUSIONS: The GDC models had a small enough TAC noise-image misregistration (0.8%) that they have a plausible use as simulations of thyroid activity for querying performance of other models such as washout models, for altered ROI size, noise, administered dose, and image framing rates. Indeed, of the four washout-only models tested, no single model approached the apparent accuracy of the GDC model using only 90 min of data. Ninety minutes is a long gamma-camera acquisition time for a patient, but a short a time for most kinetic models. Consequently, the results should be regarded as preliminary. Springer International Publishing 2016-12-16 /pmc/articles/PMC5161052/ /pubmed/27987183 http://dx.doi.org/10.1186/s40658-016-0166-z Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Research
Wesolowski, Carl A.
Wanasundara, Surajith N.
Wesolowski, Michal J.
Erbas, Belkis
Babyn, Paul S.
A gamma-distribution convolution model of (99m)Tc-MIBI thyroid time-activity curves
title A gamma-distribution convolution model of (99m)Tc-MIBI thyroid time-activity curves
title_full A gamma-distribution convolution model of (99m)Tc-MIBI thyroid time-activity curves
title_fullStr A gamma-distribution convolution model of (99m)Tc-MIBI thyroid time-activity curves
title_full_unstemmed A gamma-distribution convolution model of (99m)Tc-MIBI thyroid time-activity curves
title_short A gamma-distribution convolution model of (99m)Tc-MIBI thyroid time-activity curves
title_sort gamma-distribution convolution model of (99m)tc-mibi thyroid time-activity curves
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5161052/
https://www.ncbi.nlm.nih.gov/pubmed/27987183
http://dx.doi.org/10.1186/s40658-016-0166-z
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